Data Mining on Student Database to Improve Future Performance

Kohli, Kashish; Birla, Shiivong

doi:10.5120/ijca2016910717

Cited by 7 publications

(1 citation statement)

References 5 publications

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“…Under the classification techniques, Neural Network and Decision Tree are the two methods highly used by the researchers for predicting students performance. Authors in [15] applied Data Mining techniques to find and evaluate future results and factors which affect them. The analysis was performed by discovering the Association rules for the same using FP Growth Algorithm which were sorted by Lift Metric.…”

Section: Iirelated Workmentioning

confidence: 99%

Analysis of Students' Performance by Using Different Data Mining Classifiers

Almarabeh¹

2017

IJMECS

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Abstract-Data mining is the analysis of a large dataset to discover patterns and use those patterns to predict the likelihood of the future events. Data mining is becoming a very important field in educational sectors and it holds great potential for the schools and universities. There are many data mining classification techniques with different levels of accuracy. The objective of this paper is to analyze and evaluate the university students' performance by applying different data mining classification techniques by using WEKA tool. The highest accuracy of classifier algorithms depends on the size and nature of the data. Five classifiers are used NaiveBayes, Bayesian Network, ID3, J48 and Neural Network Different performance measures are used to compare the results between these classifiers. The results shows that Bayesian Network classifier has the highest accuracy among the other classifiers.

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Section: Iirelated Workmentioning

confidence: 99%

Analysis of Students' Performance by Using Different Data Mining Classifiers

Almarabeh¹

2017

IJMECS

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Model of Education Technology for Language Pedagogy in Higher Education

Simanjuntak

2023

E3S Web Conf.

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Education technology enables advances in every aspect of education. This paper explored a model for language pedagogy through Educational Data Mining (EDM). EDM has offered important contributions in the last decade. With EDM, many predictions could be made in terms of learning paths, patterns for success and failure, and students’ preferences. Such predictions would be much needed for decision-making, business, and academic-wise. However, not enough EDM has been done regarding language learning. This present study provides a potential model for EDM in language pedagogy. A substantial review of the literature was complemented with samples of data from students’ language learning performance as illustrations for the model. Corpus for this study was students’ writing from various universities. Results showed the need to integrate language input, process, and output into EDM and create a base model of learning. Predictions for learning challenges, problems, and failures would be beneficial to improve teaching and learning. In conclusion, EDM was inevitably needed in the rise of online learning. Practical implications for language platforms and digital language learning were also discussed.

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